The existing tools for the response-time analysis of Controller Area
Network (CAN) support only periodic and sporadic messages. They do not
analyze mixed messages which are implemented by several higher-level
protocols based on CAN that are used in the automotive industry. We
present a new response-time analyzer for CAN that supports periodic
and sporadic as well as mixed messages. Moreover, it supports the
analysis of the system where periodic and mixed messages are scheduled
with offsets. It will support the analysis of all types of messages
while taking into account several queueing policies and buffer
limitations in the CAN controllers.

Understanding distributed systems is a complex task. There are many
subsystems involved, such as network equipment, disk and CPU, which
effect behavior. In order to analyze this kind of applications,
different approaches have been proposed: simulation, emulation and
experimentation. Each paradigm has evolved independently, providing
their own set of tools and methodologies.

This paper explores how these tools and methodologies can be combined
in practice. Given a simple question on a particular system, we
explore how different experimental frameworks can be combined in
practice. We use a representative framework for each methodology:
Simgrid for simulation, Distem for emulation and Grid’5000 for
experimentation. Our experiments are formally described using the
workflow logic provided by the XP Flow tool.

Our long term goal is to foster a coherent methodological framework
for the study of distributed systems. The contributions of this
article to that end are the following: we identify a set of pitfalls
in each paradigm that experimenters may encounter regarding models,
platform descriptions and others. We propose a set of general
guidelines to avoid these pitfalls. We show these guidelines may lead
to accurate simulation results. Finally, we provide some insight to
framework developers in order to improve the tools and thus facilitate
this convergence.

Simulation is the imitation of a system or a process in order to
manage the complexity of simulated system or to optimize its
performance. This paper presents a agent-based strategy of modeling
and simulation.We introduce some modeling methodologies in order to
determine the most adequate technique to deal with embedded control
systems. We also introduce the Tropos and Agentology methodologies by
describing used concepts and how they are integrated with the current
stages of Tropos and Multi-agent System methodology. The above is
illustrated using an embedded real-time control system as a case
study.